Robustness test approach combining binary sample splitting and system of equations methods

MA Jian, HU Yi, LIU Jinquan

Systems Engineering - Theory & Practice ›› 2020, Vol. 40 ›› Issue (6) : 1371-1381.

PDF(737 KB)
PDF(737 KB)
Systems Engineering - Theory & Practice ›› 2020, Vol. 40 ›› Issue (6) : 1371-1381. DOI: 10.12011/1000-6788-2020-0432-11

Robustness test approach combining binary sample splitting and system of equations methods

  • MA Jian1, HU Yi2,3, LIU Jinquan1
Author information +
History +

Abstract

This article proposes a novel robustness test method which combines binary sample splitting and system of equations techniques. First, we build a structural model to analyze relationship between the conditional exogeneity assumption and the explanations of structural parameters, thus establish principles of construction of alternative models. Then, we propose two construction methods:Binary sample splitting and confounding variables adjusting. Finally, we propose a robustness test statistic which based on system of equations method. Furthermore, we adjust computation process of the test to avoid singularity issue. Our Monte Carlo experiments verify the finite sample properties of this robustness test. As an application, we analyze the model settings of an article by Professor Acemoglu, which studied the influence of aging on economic growth.

Key words

structural equation model / robustness / binary sample splitting / system of equations / singularity

Cite this article

Download Citations
MA Jian , HU Yi , LIU Jinquan. Robustness test approach combining binary sample splitting and system of equations methods. Systems Engineering - Theory & Practice, 2020, 40(6): 1371-1381 https://doi.org/10.12011/1000-6788-2020-0432-11

References

[1] Friedman J, Hastie T, Tibshirani R. The elements of statistical learning[M]. New York:Springer New York Inc, 2009.
[2] Leamer E E. Questions, theory and data[C]//Medema S G, Samuels W J. Foundations of Research in Economics:How do Economists Do Economics? Northampton:Edward Elgar Publishing Co, 1996:175-190.
[3] Brodeur A, Le M, Sangnier M, et al. Star wars:The empirics strike back[J]. American Economic Journal:Applied Economics, 2016, 8(1):1-32.
[4] Leamer E E. Let's take the con out of econometrics[J]. The American Economic Review, 1983, 73(1):31-43.
[5] White H. A reality check for data snooping[J]. Econometrica, 2000, 68(5):1097-1126.
[6] Kennedy P E. Sinning in the basement:What are the rules? The ten commandments of applied econometrics[J]. Journal of Economic Surveys, 2002, 16(4):569-589.
[7] Angrist J D, Pischke J S. The credibility revolution in empirical economics:How better research design is taking the con out of econometrics[J]. Journal of Economic Perspectives, 2010, 24(2):3-30.
[8] 李子奈, 李鲲鹏. 关于计量经济学模型随机扰动项的讨论[J]. 统计研究, 2009, 26(2):62-67. Li Z N, Li K P. Discussion about the stochastic disturbance term of econometric models[J]. Statistical Research, 2009, 26(2):62-67.
[9] 王美今, 林建浩. 计量经济学应用研究的可信性革命[J]. 经济研究, 2012, 47(2):120-132. Wang M J, Lin J H. The credibility revolution in applied econometric research[J]. Economic Research Journal, 2012, 47(2):120-132.
[10] 张进峰, 方颖. 空间滞后模型的稳健检验[J]. 系统工程理论与实践, 2012, 32(1):76-82. Zhang J F, Fang Y. Robust test for spatial lag model[J]. Systems Engineering-Theory & Practice, 2012, 32(1):76-82.
[11] 郑振龙, 孙清泉. 基于第一HJ距离的线性因子模型设定检验[J]. 管理科学学报, 2017, 20(1):108-128. Zheng Z L, Sun Q Q. Linear factor models' model specification test based on the first HJ distance[J]. Journal of Management Sciences in China, 2017, 20(1):108-128.
[12] Athey S, Imbens G W. A measure of robustness to misspecification[J]. The American Economic Review, 2015, 105(5):476-480.
[13] 马键, 林建浩, 胡毅. 处置效应模型设定的稳健性评估[J]. 统计研究, 2018, 35(8):116-128. Ma J, Lin J H, Hu Y. A robustness evaluation method for treatment effect estimation[J]. Statistical Research, 2018, 35(8):116-128.
[14] Lu X, White H. Robustness checks and robustness tests in applied economics[J]. Journal of Econometrics, 2014, 178(1):194-206.
[15] Ma J, Lin J. Comment on "Robustness checks and robustness tests in applied economics" by X. Lu and H. White[R]. Working Paper, 2018:1-12.
[16] Wooldridge J M. Econometric analysis of cross section and panel data[M]. 2nd ed. Cambridge:MIT Press, 2010.
[17] Greene W. Econometric analysis[M]. 7th ed. Upper Saddle River:Prentice Hall, 2011.
[18] Rosenbaum P R. The consquences of adjustment for a concomitant variable that has been affected by the treatment[J]. Journal of the Royal Statistical Society. Series A (General), 1984, 147(5):656-666.
[19] Wooldridge J M. Violating ignorability of treatment by controlling for too many factors[J]. Econometric Theory, 2005, 21(5):1026-1028.
[20] Dawid A P. Conditional independence in statistical theory[J]. Journal of the Royal Statistical Society. Series B (Methodological), 1979, 41(1):1-31.
[21] Wooldridge J M. Selection corrections for panel data models under conditional mean independence assumptions[J]. Journal of Econometrics, 1995, 68(1):115-132.
[22] White H, Chalak K. Testing a conditional form of exogeneity[J]. Economics Letters, 2010, 109(2):88-90.
[23] Imbens G W, Wooldridge J M. Recent developments in the econometrics of program evaluation[J]. Journal of Economic literature, 2009, 47(1):5-86.
[24] Pearl J. Causality[M]. 2nd ed. Cambridge:Cambridge University Press, 2009.
[25] Acemoglu D, Restrepo P. Secular stagnation? The effect of aging on economic growth in the age of automation[J]. American Economic Review, 2017, 107(5):174-179.
[26] Pearl J, Mackenzie D. The book of why:The new science of cause and effect[M]. New York:Basic Books, 2018.
[27] Hausman J A. Specification tests in econometrics[J]. Econometrica, 1978, 46(6):1251-1271.
[28] White H. Asymptotic theory for econometricians[M]. San Diego:Academic press, 2014.

Funding

National Natural Science Foundation of China (71503056, 71873042); National Social Science Foundation of China (19AJY005); China Scholarship Council (201608440100)
PDF(737 KB)

536

Accesses

0

Citation

Detail

Sections
Recommended

/